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import streamlit as st
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
from langchain_core.prompts import PromptTemplate
from langchain_core.runnables import RunnableSequence
from langchain_huggingface import HuggingFacePipeline
from langchain.agents import create_react_agent, AgentExecutor, Tool
from langchain.memory import ConversationBufferMemory
# Mock lead data
LEADS = [
{"name": "John Doe", "email": "[email protected]", "company": "TechCorp"},
{"name": "Jane Smith", "email": "[email protected]", "company": "InnoSoft"},
{"name": "Bob Johnson", "email": "[email protected]", "company": "DataTech"},
]
# Set up the open-source LLM
@st.cache_resource
def load_model():
model_name = "google/flan-t5-large"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
pipe = pipeline(
"text2text-generation",
model=model,
tokenizer=tokenizer,
max_length=512
)
return HuggingFacePipeline(pipeline=pipe)
local_llm = load_model()
# Define the tools for the agent
def search_leads(query):
return [lead for lead in LEADS if query.lower() in lead['name'].lower()]
def send_email(to_email, subject, body):
# For demo purposes, we'll just print the email details
st.write(f"Email sent to: {to_email}")
st.write(f"Subject: {subject}")
st.write(f"Body: {body}")
return "Email sent successfully"
tools = [
Tool(
name="Search Leads",
func=search_leads,
description="Useful for searching leads by name"
),
Tool(
name="Send Email",
func=send_email,
description="Useful for sending emails to leads"
)
]
# Set up the agent
prefix = """You are an AI CyberSecurity Program Advisor. Your goal is to engage with leads and get them to book a video call for an in-person sales meeting. You have access to a list of leads and can send emails.
You have access to the following tools:"""
suffix = """Begin!
{chat_history}
Human: {human_input}
AI: Let's approach this step-by-step:"""
prompt = PromptTemplate(
template=prefix + "{agent_scratchpad}" + suffix,
input_variables=["human_input", "chat_history", "agent_scratchpad"]
)
llm_chain = RunnableSequence(prompt, local_llm)
memory = ConversationBufferMemory(memory_key="chat_history")
agent = create_react_agent(local_llm, tools, prompt)
agent_executor = AgentExecutor.from_agent_and_tools(
agent=agent, tools=tools, verbose=True, memory=memory
)
# Streamlit interface
st.title("AI CyberSecurity Program Advisor Demo")
st.write("This demo showcases an AI agent that can engage with leads and attempt to book video calls for in-person sales meetings.")
lead_name = st.text_input("Enter a lead's name to engage with:")
if lead_name:
lead_info = search_leads(lead_name)
if not lead_info:
st.write(f"No lead found with the name {lead_name}")
else:
lead = lead_info[0]
st.write(f"Lead found: {lead['name']} (Email: {lead['email']}, Company: {lead['company']})")
initial_message = f"Hello {lead['name']}, I'd like to discuss our cybersecurity program with {lead['company']}. Are you available for a quick video call?"
if st.button("Engage with Lead"):
with st.spinner("AI is generating a response..."):
response = agent_executor.run(initial_message)
st.write("AI Response:")
st.write(response)
st.sidebar.title("About")
st.sidebar.info("This is a demo of an AI CyberSecurity Program Advisor using an open-source LLM and LangChain. It's designed to engage with leads and attempt to book video calls for sales meetings.")
# To run this script, use: streamlit run your_script_name.py |